protection of soil from the loss of organic carbon by taking into account erosion and managing land...
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GLOBAL SYMPOSIUM ON SOIL ORGANIC CARBON, Rome, Italy, 21-23 March 2017
Protection of soil from the loss of organic carbon by taking into account erosion and managing land use at varying soil type:
indication from a model semiarid areaSergio Saia1*, Calogero Schillaci2,3, Aldo Lipani4, Maria Fantappiè5, Michael Märker3,6, Luigi Lombardo7, Maria G. Matranga8, Vito Ferraro8, Fabio Guaitoli9, Marco Acutis2 1 [Italy Council for Agricultural Research and Economics (CREA), Cereal Research Center (CREA-CER), Foggia, Italy, [email protected], *corresponding author] 2 [Department of Agricultural and Environmental Science (DISAA), University of Milan Via Celoria 2, 20133 Milan, [email protected]]3 [Department of Geosciences, Tübingen University, Germany Rümelinstr 19-23 Tübingen, Germany]4 [Institute of Software Technology and Interactive Systems, TU Wien, [email protected]]5 [Council for Agricultural Research and Economics (CREA), Centre for Agrobiology and Pedology (CRA-ABP), Florence, Italy, [email protected]]6 [Department of Earth and Environmental Sciences, University of Pavia, Italy. [email protected]] 7 [Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Tuwal, Jeddah. [email protected]]8 [Regional Bureau for Agriculture, Rural Development and Mediterranean Fishery, the Department of Agriculture, Service 7 UOS7.03 Geographical Information Systems, Cartography and Broadband Connection in Agriculture, Palermo, [email protected], [email protected]]9 [Regional Bureau for Agriculture, Rural Development and Mediterranean Fishery, the Department of Agriculture, Service 5 UOS5.05 - Valutazione territoriale e gestione del rischio in agricoltura, , [email protected]] [email protected]
State of the artDrylands cover nearly of the half of the world and are inhabited by ca. 40 % of the world’s population.
In such lands, net primary and agricultural production is limited by
- water scarcity and high temperatures;
- low water holding capacity (WHC) and low fertility of the soil and other soil-specific traits (e.g. soil organic carbon [SOC]);
- soil erosion.
- fragile (agro-)ecosystems;
State of the art
World Soil C stock (Minasny et al. 2017)
State of the art
Preservation/increase of the soil organic carbon (SOC)
Potential to mitigate the loss of fertility and thus yield, and increase the CO2
sequestration in soil.
This implies that SOC management plays a direct and crucial role in the world economy and is strategic to combat hunger and poverty.
State of the artSeveral measures can be adopted at wide scale to mitigate loss of SOC and preserve soil ecosystem service:
• Managing land use/land cover;• Choice of crop species and genotypes;• Reduction of Soil tillage and other agronomical management techniques;
these interact with climate and soil conditions (e.g. texture, etc)&
also depends on the gross income of the population in the area and nation.
The ability to indicate site-specific SOC management strategies also rely on availability of data of SOC and its ancillary variables
Aim of the studyHere we used legacy data of a reference semiarid area (Sicily, Italy) to estimate the importance of land use and soil erosion potential on SOC variation in time and space at varying soil type and aridity of the environment.- A total of 25,286 km2, 60% of which cultivated.- A total of ca. 2700 geo-referenced (more than 1 point each 10 km2) observation of SOC
and 1049 of bulk density;- mean annual temperatures of 1.8 °C to 15.0 °C and mean annual precipitation from 350 to
1300 mm;- Several soil orders. Dominant soils (World Reference Base) are Regosols, Calcisols,
Vertisols, Andosols, Leptosols, Phaeozems and Cambisols;
Sicily has great potential as an open laboratory for studies about ecological issues and anthropic pressure on the agro-ecosystems thanks to the variability of its traits and deep knowledge of its soils.
Materials and MethodsLegacy dataset provided by the Regional Bureau for Agriculture, Rural Development and Mediterranean Fishery, the Department of Agriculture, and Service 7 UOS7.03• 2700 soil profiles with SOC observations + texture + actual land use• 1049 of bulk density
Materials and MethodsPredictors of SOC:• Climatic data from Worldclim (1-km resolution): mean annual temperature and rainfall;
• Land covers from CORINE;
• Remote sensing-derived predictors consisted of the LANDSAT 5 spectral bands and the Normalized Difference Vegetation Index (NDVI)
• Shuttle Radar Topography Mission digital elevation model (Sept. 2014, 1-arcsec spatial resolution) for the morphometric spatial predictors. Eleven terrain attributes: • 1) slope, • 2) catchment area, • 3) aspect, • 4) plan curvature, • 5) profile curvature, • 6) length-slope factor,
7) channel network base level, 8) convergence index, 9) valley depth, 10) topographic wetness index, 11) landform classification.
Materials and Methods• Application of 2 Regression Trees modelling for spatialization and handling of not
Gaussian data and have a powerful ecological/anthropic insight on SOC space-time variation:• Boosted Regression Trees (BRT; Elith et al., 2008) for C concentration and space-time
change (from 1993 to 2008);• Stochastic Gradient Treeboost (SGT; Friedman, 2002) for C stock after application of a
pedotransfer function (Pellegrini et al., 2007) for missing bulk density points.
• Dissection of the variations per land use groups: • ARA: arable land, mostly cropped with cereals, grain and forage legumes; • VFO: vineyards, fruit trees and berry plantations, and olive groves;• CCP: annual crops associated with permanent crops, complex cultivation patterns,
land principally occupied by agriculture, with significant areas of natural vegetation.
Results - Carbon Stock map
Our map
ISRIC map
GSOC mapHeiderer & Kocky 2012
Soilgrid.com
Results - Predictors importance for Carbon Stock • 71.4% of points predicted in the range of
±50% than observed (light green point)
• SGT R2 = 0.470• High land use importance (orange bar)
SOC tha-1
>
Results – C concentrationChange from 1993 to 2008
1993 – 785 observations 2008 – 337 observations
Results – SOC predictorsHigh land use and texture importance Measures related to erosion
Remote sensed predictors reduced variability of models
Results – SOC variation from 1993 to 2008
Change in SOC from 1993 to 2008 (in g C kg-1 soil)
R2=0.63-0.69
SOC reduced in high SOC soils and increased
in low SOC soil
Results – SOC variation from 1993 to 2008
SOC change map matched that of
Erosion
Results – SOC variation from 1993 to 2008
…but also depended on land use
Results – SOC variation from 1993 to 2008SOC increased, but its rate was far from the 4 per mille initiative
Results – SOC variation from 1993 to 2008with no interaction
with soil texture
SOC decresed by 65.3±8.4 mg C per 1% point of Clay increase
in all Land use
ARABLES (ARA) y = -0.0061x + 1.4717R² = 0.0439
TREE CROPS (VFO) y = -0.0063x + 1.5605R² = 0.0438
NATURAL (NAT) y = -0.0063x + 1.505R² = 0.0539
ALL DATAPOINTS y = -0.0065x + 1.5084R² = 0.0556
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 15 30 45 60 75
SOC
%
CLAY %
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 15 30 45 60 75
SOC
%
CLAY %
SOC (%) at varying clay content (%)
Conf. Int. 90%Pred. Int. 90%data points
Summary- SOC reduced in high SOC soils and increased in low SOC soil;
- Land use, texture, rainfall and measurements related to erosion and deposition were strong predictors of SOC;
- SOC increased in arables (ARA) and tree-crops (VFO) more than natural and semi natural environments (NAT);
- Map of SOC variation matched that of soil erosion (Fantappiè et al., 2015) and temperature and rainfall trends in the last 25 years (Cannarozzo et al., 2006; Viola et al., 2014, not shown).
Conclusions & Future prospects- Need of using highly performing models to address decision making
on soil at the sub- regional level- Focusing on land use management in agricultural areas is a valuable
tool to increase SOC
- To directly model variation of SOC and other soil properties
- To study the relationship of computed and modelled SOC variation with measured and prospected variation of SOC predictors in climate change scenario
Thanks for the attention I wish to thanks all the authors contributing to this work